Concept-to-text generation refers to the task of automatically producing textual output from non-linguistic input. We present a joint model that captures content selection (?what to say?) and surface realization (?how to say?) in an unsupervised domain-independent fashion. Rather than breaking up the generation pro- cess into a sequence of local decisions, we de- fine a probabilistic context-free grammar that globally describes the inherent structure of the input (a corpus of database records and text describing some of them). We represent our grammar compactly as a weighted hypergraph and recast generation as the task of finding the best derivation tree for a given input. Experi- mental evaluation on several domains achieves competitive results with state-of-the-art sys- tems that use dom...